Types of Investors
The needs of investment clients vary widely, but we can group investors into two broad categories – individual and institutional investors. Different investors will have varying investment time horizons, tolerance for portfolio risk, income, and liquidity needs. Individual Investors Individual…
Portfolio Approach to Investing
Investors have to ensure their investments achieve their future needs. A portfolio approach to investment decision-making is important regardless of future financial goals. It enables an investor to create a diversified investment portfolio. Portfolio Diversification The benefits of a diversified portfolio…
Calculation and Interpretation of Confidence Intervals
Confidence interval (CI) refers to a range of values within which statisticians believe the actual value of a certain population parameter lies. It differs from a point estimate which is a single, specific numerical value.
T-distribution
The student’s T-distribution is a bell-shaped probability distribution symmetrical about its mean. It is considered the best distribution to use for the construction of confidence intervals when:
Point Estimate vs. Confidence Interval Estimate
Point Estimate A point estimate gives statisticians a single value as the estimate of a given population parameter. For example, the sample mean X̄ is the point estimate of the population mean μ. Similarly, the sample proportion p is a…
Desirable Properties of an Estimator
A point estimator (PE) is a sample statistic used to estimate an unknown population parameter. It is a random variable and therefore varies from sample to sample. A good example of an estimator is the sample mean x, which helps…
Calculation and Interpretation of the Standard Error of the Sample Mean
The standard error (SE) of the sample mean refers to the standard deviation of the distribution of the sample means. It gives analysts an estimate of the variability they would expect if they were to draw multiple samples from the…
The Central Limit Theorem
The central limit theorem asserts that when we have simple random samples each of size n from a population with a mean μ and variance σ2, the sample mean X approximately has a normal distribution with mean μ and variance…
Simple Random vs. Stratified Random Sampling
Simple random and stratified random sampling are both sampling techniques used by analysts during statistical analyses. Simple Random Sampling Simple random sampling involves the selection of a sample from an entire population such that each member or element of the…
